A multiscale adaptive mask method for rigid intraoperative ultrasound and preoperative CT image registration

Rigid registration of intraoperative ultrasound (US) and preoperative CT image is important for providing real-time guidance during operations. However, due to the low spatial and temporal resolutions and the dissimilarity between US and CT, accurate registration of CT and US images is still a challenging problem.

The authors propose an adaptive-mask-based CT and US registration method. The registration is initialized by matching the image regions of CT and US with intensity distinctiveness. The registration is a multistage iterative process in which the US region mask is adaptively updated. Each stage is an interleaving process of optimizing a global similarity energy and updating the mask of US by selecting high saliency and local statistical dependency regions.

Performances of their proposed method and mutual information (MI) based method are validated with simulated, in vitro phantom and real patient datasets. Results show that their method has larger capture range in all datasets. The estimated transformation parameters in their method are more accurate than the mutual information based method.

By using an adaptively updated mask of the US image, regions with salient intensity information and high intensity correlation with CT are included in the registration. Regions which have low correlation with CT such as artifacts are excluded in the registration so that the robustness and accuracy of the intensity-based registration method are improved.